Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

). These networks harbor data for 64 sites on vegetation or glacier surfaces above 60° N. Size of dots: study duration. Map coloring corresponds to the raster circumpolar Arctic vegetation map (CAVM 19 ) types: B1-4: barrens; GT and G1-4: graminoid; P1-2: prostrate-shrub; S1-2: erect-shrub; W1-3: wetlands; GL: glacier; FW: freshwater. We extracted the in situ vegetation type ('Vegetation type') from site descriptions for each study site in the in situ SEB-data (b) and literature data (c) and counted the number of sites per vegetation type. Vegetation types correspond to CAVM types (in some cases only resolvable to the main plant physiognomy without number) plus one additional class: BPB (boreal peat bog). See Supplementary Figure 2 for maps where study site locations are mapped for each vegetation type separately. Source data are provided as a Source Data file.
Supplementary Figure 3 Supplementary Figure 3. Temporal coverage of in situ observations in the SEB-data. Shown are respective times when data of either net radiation (Rnet), sensible heat flux (H) or latent heat flux (LE) is available at a specific study site. Sites are grouped according to Vegetation type; colors correspond to barren complex (purple); prostrateshrub tundra (rose); graminoid tundra (yellow green); erect-shrub tundra (dark green); wetlands (light blue); boreal peat bog (dark blue) and glacier (grey). In total, there are 64 sites covering the time 1994-2021; the average nr. of years covered by a site are 10 ± 5.87. Source data are provided as a Source Data file.
Supplementary Figure 4. Maps of ancillary geographic data used to derive surface energy budget (SEB)drivers. a CAVM raster map 19 with the CAVM types: B1-4: barrens; G1-4: graminoid; P1-2: prostrate-shrub; S1-2: erect-shrub; W1-3: wetlands; GL: glacier; FW: freshwater; SW: seawater, NAr: non-Arctic vegetation. b permafrost map 61 , with information on permafrost extent (C: continuous, D: discontinuous, S: sporadic, I: isolated patches) and ground ice content (h: high, m: medium, l: low). c altitude (m above sea level): ArcticDEM mosaic product: arcticdem_mosaic_100m_v3.0 62 . d mean annual air temperature (°C): CHELSA V2.1 submodel CMIP5 "bio10_01" variable 53,54 . e annual precipitation (mm): CHELSA V2.1 submodel CMIP5 "bio10_12" variable 55 . f cloud cover (%): "cldamt" product in ISCCP-Basic-H series: ISCCP-Basic.HGM.v01r00.GLOBAL 59,60 . g cloud-top temperature (°C) : "tc" product in ISCCP-Basic-H series: ISCCP-Basic.HGM.v01r00.GLOBAL 59,60 . The dashed line corresponds to the 60° N latitude. A list of data sources used in this study is contained in Supplementary Table 3   Reasons to exclude studies: a studies that i. did not cover terrestrial land areas (e.g. studies of marine and ocean surfaces, or studies of extra-terrestrial energy fluxes), ii. were conceptual studies without measured data, iii. did not report on any of the surface energy fluxes and components we deemed as essential (Supplementary Table 1), iv. were not peer-reviewed research articles, v. were not primary research articles (e.g. meta-analyses and reviews), vi. did only cover times before 1950. b studies that i. did not contain any estimates for specific geographic locations at local spatial scale (<10 km 2 area), ii. did not contain in situ measurements of essential surface energy fluxes and components (Supplementary Table 1), iii. covered only locations below 50° N (e.g. alpine tundra only). c Studies that i. covered only locations below 60° N, ii. did not cover surface energy flux measurements on vegetation or glacier ice (i.e. studies on forest, forest ecotone, lake, urban or crop land-cover). These eligibility criteria were developed according to the PICOS approach as follows: 1. Participants: studies reporting on essential surface energy fluxes and components (Supplementary Table 1), across the pan-Arctic and subarctic regions > 60° N, across all available years >1950; 2. Interventions & Comparators: studies reporting on SEB-drivers of interest: climate (including mean annual air temperature and annual precipitation), vegetation type, permafrost type, topography, snow characteristics and seasonality, cloud properties, and time of year. 3. Outcomes: magnitude and timing of surface energy fluxes, identity of surface energy fluxes and components, identity of SEB-drivers, latitude, longitude, land-cover type of study locations, seasons assessed, spatio-temporal extent of assessed surface energy fluxes and components and -drivers, data and methods used for inference of surface energy fluxes/components and drivers (i.e. modeled, directly measured, interpolated, remotely sensed, etc.), surface energy budget closure assumption; 4. Study design: comparative, experimental and modeling studies containing in situ measurements of surface energy fluxes and components 1 . variables considered as response variables in studies, typically on y-axes in figures. Bar height and numbers refer to the numbers of studies that contain a respective driver or response variable category. Radiative SEB contains incoming, outgoing and net radiative energy fluxes; nonradiative SEB contains convective and conductive sensible, latent and ground heat fluxes, as well as energy used for melting snow and ice; fractional SEB contains bowen ratio, evaporative fraction and energy fluxes indicated as fraction of absorbed or incoming radiation. Literature data on surface energy budgets across the Arctic was systematically searched and collected on the ISI Web of Science (Methods). We find that the majority of studies (82%) simultaneously assessed several surface energy fluxes/components and -drivers. Air temperature, radiative and non-radiative surface energy fluxes are most often assessed as response variables (bottom panel), whereas many studies assess season/daytime, climatic factors and vegetation type as drivers (top panel). An overwhelming majority of studies (139 of 148 studies, i.e. 94%), assessed time (i.e. time of year or time of day) as a driver.
Supplementary Figure 9 Supplementary Figure 9. Relationships among components and drivers of the summer surface energy budget (SEB) for the "vegetation" SEB-data subset (upper right triangles) and the "glacier" SEB-data subset (lower left triangles). Pearson correlation coefficients (r) for site-dependent SEB-drivers and mixed-model estimates of surface energy flux/component mean summer (JJA) magnitudes. Significant negative (red) and positive (blue) correlations (i.e. P-value<0.05) are indicated with colored rectangles. Light grey rectangles: insignificant relationships.
Dark grey rectangles: correlations with the same variable or correlation with fewer than 5 sites involved. Note that flux direction convention is positive away from the surface for non-radiative fluxes. a analysis with mean surface energy flux values in Wm -2 ; b analysis with normalized surface energy fluxes expressed in % of potential incoming shortwave radiation (prefix "n." for corresponding surface energy fluxes). Rnet: net radiation; SWnet: net shortwave radiation; LWnet: net longwave radiation; H: sensible heat flux; LE: latent heat flux; G: ground heat flux; Albedo: albedo; Tsurf: surface temperature; Tair: air temperature; Tsurf-Tair: difference between surface and air temperature; SEB-drivers: Altitude, Slope and North aspect: mean in surrounding area with radius of 500m; Temperature: mean annual air temperature; Summer warmth: summer warmth index; Continentality: conrad's continentality index; Cloud cover: mean cloud cover; Cloud temperature: mean cloud-top temperature; Precipitation: mean annual precipitation; Snow amount: mean annual snow water equivalent; Snow duration: median annual snow cover duration; see Methods and Supplementary Table 1 (32). The magnitude and direction of correlations differ between the vegetation and glacier datasets and significance tends to be weaker in the vegetation dataset, even though the number of study sites is comparable. In the glacier dataset, cloud and precipitation variables show strong correlations with surface energy components, in contrast to the vegetation dataset. In both datasets, Summer warmth shows strong relationships with surface energy components. Results for absolute (a) and normalized (b) surface energy fluxes are relatively similar. Source data are provided as a Source Data file. Supplementary Tables   14   Supplementary Table 1 Supplementary Table 1. List of essential surface energy fluxes and components, as well as surface energy budget (SEB)-drivers used in this study. Main category: surface energy fluxes and components: energy fluxes, atmospheric state variables and surface properties that affect or correlate with the surface energy budget 18,19 ; SEB-drivers: environmental factors that have been found to influence surface energy fluxes; detailed category: detailed categories as used in this study; variable name: abbreviations as used in this study; explanation: corresponding explanations; unit: corresponding variable unit. See Methods section for details on data processing for variable derivation. North aspect northness of slope aspect 1 if north-exposed, -1 if south-exposed snow Snow duration median yearly snow cover duration (difference between yearly doy of snowmelt and doy of snow onset) days *we derived normalized fluxes by dividing mean daily fluxes in Wm -2 by the daily maximum potential incoming shortwave radiation in Wm -2 , based on location and topographical conditions 20 and multiplying by 100 (indicated with prefix "n.", unit: %) Supplementary Table 2 Supplementary   Supplementary Table 4 Supplementary Table 4. Significant post-hoc pairwise comparisons of summer surface energy flux magnitudes among glacier surfaces and vegetation types. In order to assess the magnitude and significance of differences in summer surface energy fluxes among vegetation types and glacier surfaces (Vegetation type variable), we conducted a post-hoc pairwise comparison analysis for all linear mixed-models in Table 1 (bonferronicorrected    (flux* OR budget$ OR *balance$ OR exchange$))) AND (Arctic OR tundra OR "highlatitude")); TI=(NOT (ocean OR "sea-ice" OR glacier)); Language= all languages; Document Furthermore, we find that most Arctic vegetation types (CAVM 2 ) are only represented by a few sites in the SEB-data: all vegetation types except the "G4" type (tussock-sedge, dwarfshrub, moss tundra) are represented within 3 or less sites (Supplementary Figure 1b). Several Arctic vegetation types are not contained in the SEB-data; namely "B1", "B2a", "B2b", "B4" (barren complexes) and "G2" (graminoid, prostrate dwarf-shrub, forb, moss tundra) 2 . Barren complexes are especially dominant in the Canadian Arctic, cover around one fifth of the terrestrial Arctic area and are characterized by the dominance of non-vascular plant species, such as lichens and bryophytes 2 . These cryptogams have unique biophysical and hydrological properties exerting effects on surface energy fluxes distinct from other vegetation types [7][8][9] . For example, lichens have a relatively high shortwave reflectance, affecting albedo and therefore the total absorbed radiative energy at the surface 9 . Additionally, mosses and lichens have a relatively low thermal conductivity, which decreases ground heat fluxes compared to other vegetation types 8,9 . Furthermore, mosses and lichens lack a stomatal regulation of water vapor fluxes, and it has been shown that moss presence can increase latent heat flux depending on soil moisture conditions 8 . Finally, moss and lichen vegetation height is comparatively low, which affects the thickness and thus thermal insulation of snow cover, with consequences for soil moisture and ground heat fluxes in winter and the following spring period 10,11 . Hence, we find barren complex, and more generally, cryptogam-dominated vegetation communities to be underrepresented in the current SEB literature and in situ observations and suggest that they deserve more attention in future studies 7,12 .
Finally, the median timespan covered by study site measurements is, unsurprisingly, higher for the SEB-data (11 years) than the literature data (3 years; Supplementary Figure 1a). In the case of the SEB-data, vegetation sites often lack observations in autumn and winter seasons, which hampers robust annual estimates of the SEB and SEB closure, a common issue of interest in many studies of the SEB 13,14 . The median timespan covered by site-measurements is approximately doubled in case of glacier sites (12 years) compared to vegetation sites (6 years). Nevertheless, these time spans are both well below the timespan typically required to quantify trends and climatology of atmospheric state variables (i.e. ~30 years). Therefore, we emphasize the importance of the continued maintenance of existing in situ SEB observations for supporting climate modeling, at best throughout the year, even though we acknowledge the difficulties involved [15][16][17] .